Raw LLM Responses

Panelists and positions, in brief: Latanya Sweeney (Harvard University, Professor of Government and Technology): Argues that AI must be designed and regulated to safeguard privacy and ensure fair treatment in real-world institutions. Kate Crawford (University of Southern California, Distinguished Professor of AI): Argues that large-scale AI entrenches social and political inequalities and urges limits on data-extractive, classification-heavy uses, especially over people. Chris Callison-Burch (University of Pennsylvania, Professor of Computer and Information Science): Highlights LLMs transformative power in linguistics while pressing for careful, empirically grounded evaluation and policy for generative AI. Cynthia Rush (Columbia University, Associate Professor of Statistics): Brings a cautious, methodical focus on rigorously understanding and validating complex AI and data-driven systems through high-dimensional statistics and learning theory. Nate Soares (President, Machine Intelligence Research Institute, founded in 2000 by Eliezer Yudkowsky): Warns that self-improving advanced AI could be catastrophically dangerous and calls for urgent, coordinated global efforts to keep AI aligned with human survival. Eric Schmidt (former CEO and Chairman of Google): Predicts that rapidly advancing, self-improving AI will transform every sector and insists on strong guardrails and democratic governance to preserve human agency.